Continuous-Time Look-Ahead Optimization of Energy Storage in Real-Time Balancing and Regulation Markets

Abstract

This article proposes a novel continuous-time look-ahead optimization model for scheduling balancing energy and regulation capacity provided by energy storage (ES) devices and generating units in real-time markets. The proposed model implements the receding horizon control approach, where at each time, the operation of ES devices and generating units are co-optimized to minimize the total real-time system operation cost for providing the services over the control horizon in future. A continuous-time Gaussian process is developed to characterize the uncertainty and variability of load, which is used to update the load forecast over each control horizon. The proposed continuous-time problem is solved in a finite-dimensional function space spanned by Bernstein polynomials, which converts the problem into a solvable mixed-integer linear programming problem. The proposed model is implemented on the IEEE reliability test system. Numerical results show the optimal participation of ES in balancing and regulation markets considerably reduces the total real-time operation cost of the system.